Advances in ubiquitous mobile computing make it possible to provide networked services to a distributed, diverse network of users. The rapid development and customization of web applications serving to mobile devices, and of geo-aware systems, enables a community network to implement just-in-time knowledge-sharing and response. Social Networks are the foundation for next generation user-responsive information systems, and for collaborative problem-solving networks engaging multiple co-dependent services, using geo-locators and timestamps to coordinate timely, effective response to user needs.
Computer science professionals have addressed the potential of collective intelligence algorithms to deliver better-than-average predictions in response to generally quantitative questions, such as “What will the price of DRAM be next year?” However, insufficient attention has been paid to the complementary potential of collaborative intelligence. The subject invention uniquely integrates collective intelligence with collaborative intelligence. The anonymity of system users, who can access the system from diverse computing and mobile device client platforms, is maintained by a back end that supports collective intelligence (defined here as the collected aggregate input of many anonymous discrete responders to specific, generally quantitative, questions). Alternatively, the system can shift from anonymity to acknowledged identity, supporting social network participation that harnesses collaborative intelligence (defined here as diverse, generally non-anonymous, credited, time-stamped participation in a natural language system, which may include qualitative input).
The convergence of environmental sustainability and information technology (GREEN-IT) offers potential to harness collaborative intelligence whereby, as in social networks, unique individuals participate according to their particular expertise in large in-person gatherings, ranging from conventions and trade shows to concerts and sporting events, as well as in distributed networks comprised of diverse human and computer agents, collaboratively performing services through applications that harness diverse skills to address complex problems. Environmental emergency and remediation, e.g. to respond to an oil spill, requires coordinating distributed, cross-disciplinary teams to achieve effective collaboration amongst non-anonymous persons with diverse expertise, across different disciplines, organizations and locations. Future distributed collaborative responder systems can address a broad array of needs, ranging from service and commodity provision, to social and professional knowledge-sharing, security and safety in environmental hazards, with potential to harness geo-aware devices, sensor networks and distributed, situation-aware technology.
Efforts in the field of the current invention have focused on automating problem-solving in data processing networks such that service requesters are routed to the correct service provider agent. Typically, such systems rely on the computer system's capacity for pattern recognition. The subject invention addresses the challenge to create a system that also harnesses human pattern recognition capacities where needed and delegates to the computer only tasks that the computer can effectively perform. The subject patent includes a human-computer interface, such that the system engages both human and computer pattern recognition skills Often systems are hierarchical, with top-level decision-making agency that hands down through the system. The subject invention enables browsing, whereby the user can choose among alternatives offered. Methods exist that use an interactive, or rule-based, processor to annotate (or tag) text with the symbols and vocabulary of a hypertext markup language, enabling the user to manipulate and view that information in different formats and at different levels of detail. However, the subject invention addresses the need for methods that effectively combine automated tagging with human recognition and rating systems.
The present invention differs from the prior art in that it exploits the complementarity of collective and collaborative intelligence, which also entails the integration of computer-automated tasks (suitable for collective intelligence) with human pattern recognition (required for collaborative intelligence). To harness the collaborative intelligence of diverse participants entails automated tagging of user profiles while also crediting individual contributions in a knowledge processing system wherein users share information, personal ratings, recommendations, assessments, and other communications.